×

GENETIC BASIS OF ADAPTATION IN ARABIDOPSIS THALIANA: LOCAL ADAPTATION AT THE SEED DORMANCY QTL DOG1

Ilkka Kronholm,1,2 F. Xavier Pico ,3 Carlos Alonso-Blanco,4 Je ro me Goudet,5 and Juliette de Meaux1,6,7

1 Department Genetics and Plant Breeding, Max Planck Institute for Plant Breeding Research, DE-50829 Cologne, Germany

3 Departamento de Ecologıa Integrativa, Estacio n Biolo gica de Don ana (EBD), Consejo Superior de Investigaciones

Cientıficas (CSIC), Av. Ame rico Vespucio s/n, 41092 Sevilla, Spain 4 Departamento de Gene tica Molecular de Plantas, Centro Nacional de Biotecnologıa (CNB), Consejo Superior de

Investigaciones Cientıficas (CSIC), Madrid 28049, Spain 5 Department of Ecology and Evolution, University of Lausanne, CH-1015 Lausanne, Switzerland 6 Institute of Evolution and Biodiversity, University of Mu nster, DE-48149 Mu nster, Germany

7 E-mail: juliette.de.meaux@uni-muenster.de

Local adaptation provides an opportunity to study the genetic basis of adaptation and investigate the allelic architecture of adaptive genes. We study DELAY OF GERMINATION 1 (DOG1), a gene controlling natural variation in seed dormancy in Arabidopsis thaliana and investigate evolution of dormancy in 41 populations distributed in four regions separated by natural barriers. Using FST and QST comparisons, we compare variation at DOG1 with neutral markers and quantitative variation in seed dormancy. Patterns of genetic differentiation among populations suggest that the gene DOG1 contributes to local adaptation. Although QST

for seed dormancy is not different from FST for neutral markers, a correlation with variation in summer precipitation supports that seed dormancy is adaptive. We characterize dormancy variation in several F2 -populations and show that a series of functionally distinct alleles segregate at the DOG1 locus. Theoretical models have shown that the number and effect of alleles segregatin at quantitative trait loci (QTL) have important consequences for adaptation. Our results provide support to models postulating a large number of alleles at quantitative trait loci involved in adaptation.

KE Y W ORDS: DELAY OF GERMINATION 1 (DOG1), FST , population genetics, QST .

The genetic basis of local adaptation is one of the fundamental questions in evolutionary biology. Local adaptation occurs if se- lection is strong enough relative to gene flow and favors different phenotypes in different populations (Kawecki and Ebert 2004).

2 Current address: Institute of Evolutionary Biology, University of Edinburgh, Ashworth Laboratories, EH9 3JT Edinburgh, United Kingdom.

There is a long history of research into local adaptation, espe- cially in sessile organisms such as plants (Turesson 1922, 1925; Clausen et al. 1941). A recent meta-analysis of local adaptation in plants revealed that it is rather common but not universal, with large populations being more often locally adapted than smaller ones (Leimu and Fischer 2008).

In theoretical models, the fitness effects of new beneficial mutations fixing in a single population are expected to follow

.

an exponential distribution (Orr 1998). This prediction has been supported at least qualitatively by QTL-mapping experiments in multicellular organisms (Orr 2005). In the case of multiple popula- tions adapting to distinct optima, the distribution of fitness effects of fixed beneficial mutations is no longer strictly exponential, because alleles can migrate among populations (Griswold 2006). Few QTLs of moderate to large effect on a single adaptive trait have been predicted to explain most of the phenotypic differences between locally adapted populations (Griswold 2006). Overall, however, there is a dearth of data concerning the allelic diversity segregating at QTLs controlling adaptive traits in natural popula- tions. Are QTLs often biallelic or multiallelic? Simulations have shown that the allelic architecture can have substantial effects on adaptation (Yeaman and Guillaume 2009). Are the same QTLs involved in local adaptation throughout the species range, or are there enough potential loci that QTLs involved in adaptation are chosen randomly by selection each time?

We study the model organism Arabidopsis thaliana (Brassicaceae) because in this species the genetic basis of adaptation can be studied in great detail (Mitchell-Olds and Schmitt 2006). Arabidopsis thaliana is an annual plant that is capable of self-fertilization and outcrossing. We focus on DELAY OF GERMINATION 1 (DOG1), the first cloned locus controlling quantitative variation in seed dormancy in A. thaliana (Bentsink et al. 2006). DOG1 provides a unique opportunity to study allelic diversity at an adaptive locus. Indeed, it was found to colocalize with QTLs for germination timing and fitness in the field (Huang et al. 2010). The timing of germination influences not only seedling survival but also the expression of other life-history traits later in the plant’s life cycle (Evans and Cabin 1995; Donohue 2002; Wilczek et al. 2009). Overall evidence that it is under strong selection is compelling in A. thaliana (Griffith et al. 2004; Donohue et al. 2005b) and many other plant species (Marks and Prince 1981; Kalisz 1986; Biere 1991; Gross and Smith 1991). The timing of germination is determined to a large extent by the duration and strength of seed dormancy, a physiological process preventing the seed to germinate in the presence of permissive conditions for growth (Finch-Savage and Leubner-Metzger 2006). In A. thaliana, germination cannot be induced until dormancy has been released by a process called after ripening (Finch-Savage and Leubner-Metzger 2006). Broad genetic variation is present within A. thaliana for the length of after-ripening requirement (Evans and Ratcliffe 1972; Alonso-Blanco et al. 2003), but its variation throughout the species range has not been described. In addition, nucleotide variation segregating at the DOG1 locus has not been studied and its relevance for local adaptation has not been examined.

To investigate whether a gene is involved in local adapta- tion, a comparative analysis of genetic divergence across various loci measured by FST can be useful (reviewed in Holsinger and

Weir 2009). Demographic processes are expected to influence allele frequencies and phenotypic diversity, masking the action of geographically heterogeneous selection. Yet, the effect of demog- raphy is expected to be the same for the whole genome. Natural selection, by contrast, is predicted to have the greatest influence on allele frequencies of the loci under selection (Charlesworth et al. 1997; Beaumont 2005). At the phenotypic level, genetic diver- gence in quantitative traits can also be quantified using QST , a measure analogous to FST (Spitze 1993). If divergence is greater at quantitative traits than at neutral markers (QST > FST ), it is possible to make inferences about the action of geographi- cally heterogeneous selection and local adaptation (Lande 1992; Whitlock 1999). To maximize our chance to hit the spatial scale at which local adaptation can be detected, we used a sample of A. thaliana populations collected in four broad regions separated by natural barriers. Genetic variation in this sample has been charac- terized for single nucleotide polymorphism (SNP) and microsatel- lite markers distributed genome wide by Kronholm et al. (2010).

We report here a comparative analysis of phenotypic and genetic variation and address the following questions: (1) Is there a signature of local adaptation in DOG1? (2) Do we see two major functional alleles segregating at DOG1 or an allelic series with weaker and stronger alleles? (3) Can we identify the ecological forces driving adaptation at the seed dormancy locus DOG1? Methods PLANT MATERIAL For the population genetic study, we used 289 individuals col- lected in 41 populations and described in Kronholm et al. (2010). Populations are grouped in four geographically separated regions, Spain (7 populations, 70 genotypes), France (15 populations, 109 genotypes), Norway (13 populations, 64 genotypes), and Central Asia (Fig. 1). For candidate gene association (see below), 57 indi- viduals collected by M. Koornneef (MPI, Cologne) in and around Wageningen, the Netherlands, were added to increase statistical power (346 genotypes in total), but not included in the analysis of spatial patterns of variation. GENOTYPING All individuals were genotyped for 20 microsatellite markers and 137 SNP markers by Kronholm et al. (2010). These 157 mark- ers were considered to be “neutral markers” because they are distributed genome wide. Based on preliminary results, the first exon of DOG1 appeared to harbor the greatest number of polymor- phisms (M. Debieu, unpubl. results). Thus, we designed primers to amplify and sequence the first exon of DOG1, which is 393 bp long. The primers used were D1E1—5t -AAA CAC AAA CAC GCA AAC CA and i1re—5t -GCC GCA CCG TAC TGA CTA CC. PCR and Sanger sequencing was performed using standard protocols, the sequencing primer was i1re. When needed PCR

Figur e 1. Map of the populations used in this study. Inset shows the Central Asian populations.

products were cloned to a pCR®4-TOPO Vector using TOPO TA Cloning Kit (Invitrogen, Carlsbad, CA, USA) using manufactur- ers instructions. Electropherograms were inspected for errors and sequences could be aligned unambiguously using BioEdit 7.0.5.3 (Hall 1999). Sequences have been deposited to GenBank with accession numbers HQ128719-HQ129004. Some genotypes had a large length polymorphism in the first intron of DOG1. The primers ee1f—5t -CGA CGG CTA CGA ATC TTC AG and i1re (see above) were used to amplify this polymorphism. The pres- ence or absence of this insertion was scored by resolving the PCR products on a 3% agarose gel.

We genotyped additional SNP markers distributed in the vicinity of DOG1 on the A. thaliana chromosome 5. For this, we first designed primers to amplify and sequence 9 loci around DOG1 to discover SNP markers. Primer sequences and positions are presented in Table S1. The SNP discov- ery panel was composed of 16 different genotypes from dif- ferent regions, two from Wageningen, four from Spain, three from France, three from Norway, and four from Central Asia. From each of the nine sequenced fragments, 1–4 SNPs were chosen for genotyping. Pyrosequencing assays were de-

signed with the Assay Design Software 1.0.6 (Qiagen, Hilden, Germany). The SNPs were genotyped using pyrosequencing (Fakhrai-Rad et al. 2002), with the PSQ 96MA Pyrosequenc- ing system (Qiagen, Hilden, Germany). Primer sequences for the SNP markers are given in Table S2. For biotinylated primers, we used the universal primer method of Aydin et al. (2006). A uni- versal sequence was added to the 5t end of the specific primers. In the PCR reaction four primers were used, the specific primers and the universal primers with the appropriate universal primer labeled with biotin (Table S2). For some assays the four-primer reaction did not work efficiently, so two separate PCR reactions had to be performed.

POPULATION GENETICS ANALYSES All statistical analyses were done using the statistical computing language R (R Development Core Team 2006) unless otherwise stated. Measurements of genetic diversity, Nei’s gene diversity (Hs) and allelic richness (AR) were calculated using FSTAT 2.9.3 (Goudet 2001) for the microsatellite markers. AR is a measure of the number of alleles that corrects for sample size differences be- tween populations. To compare genetic diversity between groups

of populations, a permutation test, which permutes over popula- tions, implemented in FSTAT was used.

We estimated FST for the 137 SNP markers following Weir and Cockerham (1984) as in Kronholm et al. (2010). For loci with more than two alleles such as microsatellites and DOG1, we used 8ST , which takes distances between alleles into account (Micha- lakis and Excoffier 1996; Excoffier 2007). 8ST was implemented via R-scripts written by IK. 8ST is not correlated with heterozy- gosity for multiallelic loci in contrast to Wright’s FST estimate (Kronholm et al. 2010). 8ST is in fact the best estimator of com- parative analysis of FST across markers of different types because it corrects for differences in mutation rate (or heterozygosity) be- tween loci (Slatkin 1995; Kronholm et al. 2010; Whitlock 2011). To compare 8ST value of DOG1 or QST of dormancy to neutral markers, we used the empirical distribution of microsatellites and SNPs, 157 markers in total, and compared the FST or QST values of interest to the quantiles of this distribution.

We constructed a haplotype network of the first exon of DOG1 using TCS version 1.21 (Clement et al. 2000). TCS im- plements a maximum parsimony method to infer the evolutionary relationships between the haplotypes. In analyses that required an outgroup, we used the first exon sequence of DOG1 from A. lyrata. Sequence diversity indices were calculated using DnaSP version 4.10.4 (Rozas et al. 2003). DnaSP was also used to estimate the minimum number of recombination events in exon 1 of DOG1.

PHENOTYPING AND QUANTIFICATION OF SEED DORMANCY For the common garden experiment all lines were first multiplied by selfing in the greenhouse under the same environmental con- ditions to remove any possible maternal effects. All plants were grown in the same climatized greenhouse set at +20◦ C during the day and +18◦ C during the night. Natural light was supple- mented with lamps to reach a photoperiod of 16 h of light when necessary. Plants were grown in a soil mixture (70% peat, 20% sand, and 10% clay) in 6 cm diameter round pots with one plant in each pot. The common garden experiment was started in the fall of 2007. Three plants (replicates) from the selfed progeny of each genotype (346 in total) were grown in a randomized block design (1038 plants in total). Seventeen plants died before flower- ing, and this resulted in complete loss of phenotypic information for three genotypes. Because the maternal environment can af- fect seed dormancy (Munir et al. 2001; Donohue et al. 2005a), seeds for all genotypes should mature in similar environmental conditions and thus flower simultaneously. To synchronize flow- ering time, we planted the genotypes in three different groups. The seeds were water imbibed and stratified (cold treatment at +4◦ C) in the dark for 4 days to induce germination. Thereafter, they were potted and moved to the greenhouse. After 14 days, the plants were vernalized for 28 days, in a climate chamber at +4◦ C,

under short days (8 h light) and then moved back to the green- house. Due to shifted planting of very late flowering genotypes and 4-week rosette vernalization, we were able to synchronize flowering so that most seeds matured during March–April 2008. See Supporting information and Figure S1 for details. Ripen- ing of the siliques (fruits in Brassicaceae) was assessed visually by observing a color change from green to brown. Arabidopsis thaliana produces siliques over a long period of time, and these were harvested when there were enough ripened siliques on the plant (usually siliques were harvested from the main stem). After ripening occurred in room temperature and seeds were stored in paper bags. On the day the seeds were harvested, the germination experiment was started.

To measure seed dormancy, we measured the ability of the seeds to germinate in a time course experiment performed for each seed batch (replicate) following Alonso-Blanco et al. (2003). For each time point, a sample of approximately 50–100 seeds were sown on a small petri dish, with filter paper. Water (700 μl) was added to imbibe seeds. Then the petri dishes were transferred to a growth cabinet with a temperature of +25◦ C during the day (12 h light period) and +20◦ C during the night. After 1 week, the number of germinated and dormant seeds was counted using a stereomicroscope. Seeds were scored as germinated when the root tip had protruded the seed coat. For each seed batch germination tests were performed immediately after harvest (0 weeks) and then subsequently 1, 2, 4, 8, 16, 24, 32, 40, and 52 weeks after harvest. When a seed batch was germinating at 100% in two consecutive tests, it was considered to have lost dormancy. The germination experiment was stopped after 52 weeks. A viability test was per- formed for seed batches that had not reached 100% germination following Cadman et al. (2006) (see Supporting information). We found that these seeds were still viable.

To quantify seed dormancy for a given replicate, we fol- lowed Alonso-Blanco et al. (2003). We fitted a binomial regres- sion through the germination data for each replicate, using a logit link function (Venables and Ripley 2002). From the fitted func- tion, we calculated the time for which the probability of germi- nation is 0.25, 0.5, or 0.75, referred as D25, D50, and D75. This is a measure of the time of dry storage required to reach a given probability of germination (weeks of dry storage, WODS). This transformation is particularly well suited for time course experi- ments measuring variations in proportions (Crawley 2005). Three estimates were used, to capture different aspects of dormancy. D25 is a time point at which early germinants appear, and D75 is a time point at which germination is nearly completed. We also used a linear model to quantify seed dormancy and got very similar results (see Supporting information for details).

Genotype means were estimated using a linear model yijk = μ + gi + bj + eikj , where yijk is the phenotypic observation of the kth replicate of the ith genotype in block j, μ is the overall

=

E

G

GW

mean, gi is the genotypic effect of the ith genotype, bj is the block effect for the jth block, and eikj is the residual. Genotypic means are obtained from the term μ + gi and thus possible block ef- fects are subtracted from the genotype means. In general, block effects were absent or very small and do not affect any biological conclusions of this study (see Supporting information). To investi- gate differences between populations and regions we used a linear model yijk = μ + ri + pij + eijk , where yijk is the mean phenotype of the kth genotype in the jth population within the ith region, μ is the overall mean, ri is the effect of the ith region, pij is the effect of the jth population nested within the ith region, and eijk is the residual. The estimation of heritabilities and QST is described at the end of the section Methods.

ASSOCIATION BETWEEN DOG1 AND SEED DORMANCY We tested whether genetic variation in DOG1 is associated with phenotypic variation in seed dormancy. FST between these A. thaliana populations is usually high (Kronholm et al. 2010). To avoid spurious marker–phenotype associations that arise when some alleles are associated with certain populations, population structure has to be corrected for. We performed an association test using mixed model association following Yu et al. (2006) using the PKT method of Stich et al. (2008) to control for pop- ulation structure and kinship of individuals within populations. Thus related genotypes are accounted for. The SNP markers were used in determining the optimal value of T for the kinship matrix (Table S3). Detailed description of the model is given in Support- ing information. Correcting for population structure is important

(haplotype 1) × Fet-6 (haplotype 5), both from France, (size of F2 - population N = 133); Cam-4 (haplotype 15) × Fet-6 (haplotype 5), both from France (N = 126); Cam-4 (haplotype 15) × All2-1 (haplotype 1), both from France (N = 145); Kon-2-2 (haplotype 19) × Fet-6 (haplotype 5), from Norway and France, respectively (N = 121); Kon-2-2 (haplotype 19) × Nfro-1-4 (haplotype 18), both from Norway (N = 122). F1 individuals were allowed to self to produce F2 seeds. Leaves were collected from F2 individuals for DNA extraction after flowering. To genotype DOG1 in the F2 populations, pyrosequencing assays were designed for DOG1 SNP markers distinguishing segregating haplotypes. Primers for these assays are given in Table S2. Dormancy was measured in F3 seeds collected in a common garden experiment similar to the one described above (see Supporting information) and associated with the DOG1 genotype of the corresponding F2 individual. For this, the F2 populations were analyzed using a linear model yij = μ + gi + eij , where yij is the phenotypic observation of the jth line in genotypic class i, gi is the effect of the ith DOG1 genotypic class, and eij is the residual. Following Lynch and Walsh (1998), we denote genotypic values of the genotypes D1 D1 , D1 D2 , and D2 D2 as 0, (1 + k)a, and 2a, respectively. Taking the estimates of the different genotypes from the linear model, the effect of allele D2 is obtained from a = (D2 D2 − D1 D1 ) / 2 and the dominance coefficient from k = ((D1 D2 − D1 D1 ) / a) − 1. ESTIMATION OF HERITABILITIES AND QST

Broad sense heritability, which measures the proportion of ob- served variation that is genetic variation, was estimated as H 2

2

in our sample, without a correction many spurious associations σG

σ2 2 . Because A. thaliana is predominantly self-fertilizing, ge-

would be observed (Figure S2). To increase statistical power to detect significant association

between DOG1 alleles and seed dormancy, we included a sample of accessions from Wageningen. This increased the sample size to 346 genotypes. We also tested for association within the different geographic regions. In this way, associations may be revealed that are masked on the larger sample by the segregation of distinct haplotypes with similar function. After determining the optimal T

G +σE

netic variance components can be estimated in a straightforward manner from our common garden experiment. Dominance varia- tion is not defined because all lines are homozygous. Assuming complete selfing, variation between replicates within genotypes allows estimating σ2 , the environmental variance component, and variation between genotypes allows estimating σ2 , the genetic variance component. QST measures how quantitative genetic vari- ation is partitioned between populations, and was estimated as

σ2

value, the association test for DOG1 using the mixed model was Q ST = GB is genetic vari- (Bonnin et al. 1996), where σ2 σ2 2 G B

done using the program TASSEL 2.0.1 (Bradbury et al. 2007). G B +σG W

ation between populations and σ2

is genetic variation within Sequence haplotypes of the first exon of DOG1 were used as different alleles in the association study. Because there were mul- tiple tests done due to multiple alleles, we corrected for multiple testing using the Bonferroni–Holm correction (Holm 1979).

LINKAGE ANALYSIS BETWEEN DOG1 AND SEED DORMANCY IN F2 POPULATIONS To confirm some of the candidate gene associations, we con- structed F2 populations where alleles that had significant associ- ations to dormancy were segregating. The crosses were: All2-1

populations. We used two different methods to estimate the vari- ance components: a linear mixed effects model in R, from which variance components were estimated using REML (Venables and Ripley 2002) or a Bayesian method of estimating variance com- ponents implemented in WinBUGS 1.4.3 (Lunn et al. 2000). The model itself stays the same for these two methods, only the method of estimating variance components differs. For heritability, vari- ance components were estimated from a model yijk = μ + bi + gj + eijk , where yijk is the phenotypic observation of the kth repli- cate of the jth genotype in the ith block, bi is the block effect

Sum

mar

y of

DO

G1

hapl

otyp

e fre

quen

cies

in d

iffer

ent

regi

ons.

Ta

b l e

1 .

Hap

loty

pe

Reg

ion

1 2

3 4

5 6

7 8

9 10

11

12

13

14

15

16

17

18

19

20

21

22

Spai

n Fr

ance

N

orw

ay

Cen

tral

Asi

a O

vera

ll

0.06

0.

52

−

−

0.22

0.

04

−

−

0.06

−

−

−

0.01

−

−

−

0.04

−

−

−

0.2 −

−

−

0.13

−

−

−

0.06

−

−

−

0.16

−

−

−

0.04

0.

3 −

−

0.01

−

−

−

−

−

0.06

−

−

−

0.04

−

−

−

0.

01

−

−

−

−

−

−

0.03

−

0.

43

−

−

−

0.23

−

−

0.08

−

−

0.09

−

−

0.25

−

−

0.33

−

−

0.02

−

−

0.

39

−

0.17

0.21

0.

05

0.02

0.

08

0.07

0.

01

0.00

3 0.

01

0.05

0.

03

0.01

0.

02

0.02

0.

04

0.12

0.

004

0.00

4 0.

06

0.07

0.

004

0.08

0.

03

.

for the ith block, gj is the genotypic effect for the jth genotype. Blocks were included as fixed effects and genotypes as random effects. For QST this model was extended such that yijkl = μ + bi + pj + gjk + eijkl , where pj is the population effect and other terms are the same as in the previous model, for block i, pop- ulation j, genotype k nested within population, and replicate l nested within genotype. Blocks are included as fixed effects and population and genotype are random effects. Specification of the WinBUGS models was done following O’Hara and Merila (2005). Details of WinBUGS model specification and priors used are in the Supporting information. Pairwise QST between populations was estimated using REML, while QST over several populations was estimated using WinBUGS, to obtain an interval estimate for QST .

COVARIATION OF DORMANCY AND DOG1 VARIATION WITH CLIMATIC VARIABLES To find possible causes for selection, we examined if trait val- ues of the populations are related to any environmental variables. We used the program DIVA-GIS 5.2.0.2 (Hijmans et al. 2001) in combination with the 2.5 arc-minute resolution current global climate environmental data (Hijmans et al. 2005), available at www.worldclim.org. We extracted 10 climatic parameters for our populations: latitude, altitude, annual mean temperature, temper- ature seasonality, mean temperatures of the warmest or coldest quarters, annual precipitation, precipitation seasonality, and pre- cipitations over the warmest or the coldest quarters. These data were an average of the conditions in the past 50 years. Thereafter, we built a linear model that explains variation in plant traits by climatic conditions. Population means were used in this analysis (see Supporting information for details). After identifying that summer precipitation is correlated with dormancy (see results), we calculated a pairwise matrix of absolute differences between populations for this variable. A matrix of pairwise FST values of DOG1 or neutral markers between populations were correlated to a matrix of environmental distances. Mantel tests were used to assess the statistical significance of the correlations, implemented in R-package vegan (Oksanen et al. 2007).

Results POPULATION GENETICS OF DOG1 Twenty-two haplotypes could be defined for DOG1 on the basis of the sequence of its first exon and a large insertion in the first intron (Table S4). A summary of haplotype frequencies by re- gion is presented in Table 1. In total, 11 haplotypes were present in the Spanish populations, six in the French and Norwegian populations, and three in the Central Asian populations. Differ- ent haplotypes were at high frequency in different regions. In

Figur e 2. Haplotype network of DOG1. Each node represents a single mutation; the radius of the circle is proportional to the frequency of that haplotype. The sample was the population sample and some accessions from Wageningen.

Spain, haplotypes 5, 9, 10, and 14 were at moderate frequencies, while other haplotypes present in Spain were at low frequencies. In France there were two predominant haplotypes, 1 and 15. In Norway three haplotypes, 2, 18, and 19 were at high frequen- cies. Finally, in the Central Asian populations haplotypes 4 and 21 were at nearly equal high frequencies and 22, also was at moderate frequency.

DOG1 haplotype diversity (Hd) was 0.87 in Spain, 0.62 in France, 0.77 in Norway, and 0.64 in Central Asia. For microsatel- lite markers AR was 2.269, 1.720, 1.245, and 1.383 for the Span- ish, French, Norwegian, and Central Asian populations, respec- tively. Except when comparing the Central Asian populations to those of Norway and France, differences in AR were significant (P < 0.05, 1000 permutations). Only one recombination event

could be detected between haplotypes 2 and 22, which have a mutation at position 2 (see Supporting information for details). These two haplotypes segregate at low frequency in our sample and are found in different regions (Table 1).

The haplotype network of DOG1 is presented in Figure 2. The A. lyrata outgroup cannot be joined to the network with 95% confidence. The Spanish haplotypes are mostly found in the central part of the network, while haplotypes from other re- gions occupy the peripheral parts of the network. The closely related haplotypes 18 and 19, which are found only in Norway and at high frequency, are connected to haplotype 5 by a long branch. Haplotypes 15 and 1 that are common in France are not closely related to each other, unlike haplotypes 4, 21, and 22 which are common in the Central Asian populations. The common

Freq

uenc

y

I LKKA KR ONHOLM E T A L .

30

25

20 DOG1

15

10

5

0

0.0 0.2 0.4 0.6 0.8 1.0

FST | ΦST

Figur e 3. Genetic differentiation in DOG1 was compared to the FST distribution of 157 neutral markers, microsatellites and SNP markers. The histogram is the distribution of FST for neutral mark- ers. Solid line is the 8ST of DOG1, dashed lines denote the quantiles of the neutral distribution. Values on x-axis are FST values for SNP markers, 8ST values for microsatellites and DOG1 haplotypes.

haplotypes in France are present in Spain at low frequencies (Table 1, Fig. 2).

While the restricted geographic distribution of DOG1 hap- lotypes reveals the possibility that there is local adaptation, this could be a result of drift and restricted gene flow. Therefore, we tested whether genetic differentiation in DOG1 is higher than expected by chance alone. 8ST for DOG1 was 0.8502 for all 35 European populations and 0.8769 when the Central Asian populations were included. These values lie in the tails of the

neutral marker distribution (Fig. 3). The probability of observing equal or greater values was 0.0064 for the European populations and 0.0127 when all populations were included. When consider- ing only the Spanish and the French populations or the French and the Norwegian populations, 8ST for DOG1 was 0.7432 and 0.9094, respectively. In both of these cases, DOG1 lies at the tail of the neutral marker distribution and there are only two markers with higher FST values. Within each of the geographic regions 8ST values for DOG1 are 0.4810, 0.7421, 0.9459, and 0.9559 for Spain, France, Norway, and Central Asia, respectively. However, in these cases DOG1 does not have a different value from the FST

of neutral markers. If DOG1 is under selection, population genetic theory pre-

dicts that there should be a peak of FST at the position of DOG1, when genetic divergence is viewed along the chromosome (Charlesworth et al. 1997). We tested this by using SNP haplo- types around the position of DOG1. It is clear that 8ST peaks at the position of DOG1 and then decreases to levels expected from the neutral markers (Fig. 4). Within population genetic vari- ance is included in FST measurements. This could be a problem if there are different amounts of within population genetic vari- ance in different chromosomal regions, due to differences in the amount of crossing over, for example (Charlesworth et al. 1997). Therefore, we also calculated the between-population heterozy- gosity (HT − HS ). The results show that there is also a peak for between-population heterozygosity at the position of DOG1 (Fig. 4). This suggests that the high genetic differentiation is spe- cific to DOG1. GENETIC VARIATION IN SEED DORMANCY Heritability values for seed dormancy are presented in Table 2. The heritability, calculated over all genotypes in the population

Figur e 4. Genetic differentiation along chromosome V at the position of DOG1. In panel (A), 8ST along the chromosome. The dashed lines are the upper quantiles of the neutral FST distribution. (B) HT − HS along the chromosome.

Ta b l e 2 . Heritabilities (H2 ) for seed dormancy in different re- gions, 2.5% and 97.5% denote the limits of the 95% highest pos- terior density interval. D25, D50, and D75 are seed dormancy mea-

Ta b l e 3 . Analysis of variance table for seed dormancy in different regions. Data are genotype means for D50, time taken to reach 50% germination.

surements, defined as time taken to reach 25%, 50%, or 75% germination, respectively.

Region Trait H2 2.5% 97.5%

df F-value P-value Region 3 42.872 <2.2×10−16

Population within region 37 12.830 <2.2×10−16

All D25 0.7829 0.7431 0.8191 Residuals 245

dormancy decreases from Southern to Northern Europe (Fig. 5). However, within all regions, there was a substantial amount of variation with differences among population means being often greater than differences between region means (Table S5). Within each region, there were some populations that had levels of dor- mancy different from the rest of the populations as well as low genetic variation. This can be an indication of local adaptation (see Supporting information).

ASSOCIATION BETWEEN DOG1 AND SEED

D50 0.8058 0.7694 0.8387 D75 0.7859 0.7464 0.8218

Spain D25 0.6000 0.4709 0.7159 D50 0.6961 0.5877 0.7889 D75 0.7423 0.6463 0.8229

France D25 0.6926 0.6002 0.7726 D50 0.7507 0.673 0.8174 D75 0.7129 0.6271 0.788

Norway D25 0.5844 0.4476 0.7072 D50 0.737 0.6345 0.8238 D75 0.7922 0.7055 0.8641

Asia D25 0.8348 0.7508 0.9002 D50 0.8468 0.7677 0.908 D75 0.7785 0.671 0.8648 DORMANCY

We tested whether allelic variation in DOG1 was associated with

phenotypic variation in seed dormancy, by performing a candidate gene association study with DOG1. First, we tested each DOG1 haplotype for association with each of the three seed dormancy estimates (D25, D50, and D75) in the whole sample. We also performed an analysis of genetic association within each of the regions (Table 4). Haplotype 4, which is present in French, Dutch, and Central Asian populations, was the most strongly associated allele. It was associated with increased dormancy. Haplotype 4 has the highest marker R2 values explaining up to 9% of the variance in the French populations. Haplotypes 6, 9, and 10 were weakly as- sociated with dormancy when only the Spanish populations were considered, although they are not significant after correcting for multiple testing (Table 4). Haplotype 13 was weakly associated with an increase of dormancy in the whole sample. Haplotype

Figur e 5. Seed dormancy (D50, time taken to reach 50% germina- tion) box plots for the four geographic regions. Data are genotype means.

sample, was around 0.8. The heritability remained high when calculated over genotypes within each of the regions (Table 2) and REML and the Bayesian methods gave nearly identical results. High heritability values show that the observed differences in seed dormancy between the different genotypes were mostly due to genetic variation.

There were significant differences in seed dormancy both between regions, and between populations within regions (Fig. 5, Table 3). The strongest seed dormancy was observed in Central Asian populations, where some genotypes were still dormant af- ter one year of after ripening. Among the European regions, seed

15 was associated with decreased dormancy in the French pop- ulations. Although, the effect of haplotype 15 is seen only for D25, it explains comparatively large amount of the variance, 5% (Table 4). Haplotypes 18 and 19 were also weakly associated with decreased dormancy in the whole sample. Haplotypes 21 and 22 were both associated with decreased dormancy in the Central Asian populations.

LINKAGE ANALYSIS OF DOG1 SEED DORMANCY To confirm some of the associations and to examine allelic effects of different DOG1 haplotypes, we performed linkage analysis between DOG1 haplotype and seed dormancy in a set of F2 pop- ulations generated by crossing parents carrying distinct alleles. DOG1 had Mendelian segregation in all crosses except in the cross Cam-4 × Fet-6 (haplotypes 15 and 5), where segregation

I LKKA KR ONHOLM E T A L .

Ta b l e 4 . Associations for DOG1 haplotypes and seed dormancy. Associations have been tested for all three time points for the whole sample and within each region. For multiple testing corrections the Bonferroni–Holm method was used.

Haplotype Sample Trait P-value P-adjusted Effect direction Marker R2

2

All

D50

0.014

0.245

Increase

0.006 D75 0.002 0.042 Increase 0.010

4 All D50 1.10×10−7 2.42×10−6 Increase 0.028 D25 2.55×10−8 5.61×10−7 Increase 0.027 D75 4.69×10−6 1.03×10−4 Increase 0.021 France D50 7.05×10−4 0.005 Increase 0.049 D25 2.35×10−5 1.65×10−4 Increase 0.088 D75 0.005 0.038 Increase 0.032

6 Spain D25 0.050 0.450 Increase 0.017 9 Spain D50 0.045 0.451 Decrease 0.016

D25 0.023 0.253 Decrease 0.022 10 Spain D50 0.023 0.255 Decrease 0.020

D25 0.023 0.253 Decrease 0.022 D75 0.025 0.270 Decrease 0.019

13 All D50 0.008 0.160 Increase 0.007 D25 0.016 0.304 Increase 0.005 D75 0.010 0.187 Increase 0.007

15 France D25 0.002 0.011 Decrease 0.050 18 All D50 0.010 0.184 Decrease 0.007

D25 0.014 0.288 Decrease 0.005 D75 0.007 0.124 Decrease 0.008

19 All D50 0.015 0.252 Decrease 0.006 D25 0.016 0.304 Decrease 0.005 D75 0.039 0.620 Decrease 0.005

21 Central Asia D50 0.001 0.003 Decrease 0.025 D25 0.005 0.009 Decrease 0.019 D75 1.84×10−4 5.52×10−4 Decrease 0.039

22 All D50 1.63×10−5 3.42×10−4 Decrease 0.019 D25 7.43×10−6 1.56×10−4 Decrease 0.018 D75 4.35×10−4 0.009 Decrease 0.013 Central Asia D50 0.001 0.003 Decrease 0.025 D25 0.003 0.008 Decrease 0.021 D75 2.60×10−4 5.52×10−4 Decrease 0.037

was distorted with an excess of homozygous lines (χ2 = 11.5, df = 2, P = 0.003).

DOG1 cosegregated with dormancy in all crosses except in the cross between haplotypes 18 and 19 (Table 5), thereby con- firming the significant associations reported above. Haplotypes 15 and 1 both decreased dormancy relative to haplotype 5 in F2

populations. When crossed with each other, F2 individuals with haplotype 15 had a slightly lower dormancy than those with hap- lotype 1, in agreement with association results. When haplotype 5 was crossed to haplotype 19, F2 individuals carrying haplotype 19 had a significantly lower dormancy.

Allelic effects conferred by the different haplotypes were mostly around one week, with up to 2 weeks in the F2 population in which haplotypes 15 and 5 segregated (Table 5). Dominance coefficients were very close to zero, indicating that DOG1 alle-

les behaved almost additively. In general, observed allelic effects were not as large as one could have expected from the pheno- typic differences measured for the parents in the common garden experiment. But dormancy levels of the parent lines measured in the F2 experiment were also lower than in the common garden experiment.

LOCAL ADAPTATION FOR SEED DORMANCY To test if the observed differences in seed dormancy are adaptive, QST for seed dormancy was compared to FST values from neutral markers. Although some of the observed QST values were high, they were never outside the distribution of neutral markers and the confidence intervals around these estimates were large (Table 6). QST for dormancy was always higher than 0.7 except in Spain, where QST was only 0.38 (Table 6).

229 6 EVOLUTION JULY 2012

Ta b l e 5 . Cosegregation of seed dormancy and DOG1 in F2 populations. D50 difference is the difference in the mean homozygote values for the different haplotypes. The significance of this difference was tested with a post-hoc test (Tukey HSD), corrected for multiple testing. Haplotype on the right in the third column is always the more dormant haplotype.

D50 Allelic effect, Dominance

Cross Regions Haplotypes N difference P-adjusted R2 a coefficient, k

All2-1×Fet-6

France

1 and 5

133

−1.40

5.69×10−13

0.35

0.70

−0.30 Cam-4×Fet-6 France 15 and 5 126 −4.37 7.55×10−15 0.54 2.19 −0.15 Cam-4×All2-1 France 15 and 1 145 −1.56 3.49×10−5 0.12 0.78 0.02 Kon-2-2×Fet-6 Norway×France 19 and 5 121 −2.27 4.66×10−15 0.52 1.13 −0.12 Kon-2-2×Nfro-1-4 Norway 18 and 19 122 −0.06 0.119 - - -

Ta b l e 6 . QST values for seed dormancy in different regions. 2.5% and 97.5% denote the limits of the 95% highest posterior density interval for QST . 95% FST indicates the value for the 95% quantile of neutral marker FST .

Region QST D50 2.5% 97.5% 95% FST

All

0.7523

0.6478

0.8421

0.7973 Europe 0.7053 0.5746 0.8184 0.7674 Spain 0.3815 0.1084 0.7301 0.6471 France 0.7237 0.5246 0.8785 0.7857 Norway 0.9237 0.8025 0.9911 1.0000 Central Asia 0.7912 0.5494 0.9523 1.0000

Variation for seed dormancy was also compared to envi- ronmental variation. Summer precipitation (precipitation in the warmest quarter of the year) partly explains variation in seed dor- mancy (Fig. 6), with populations that received more precipitation in the summer being less dormant. In a linear model with dor- mancy (D25) as a response, summer precipitation was significant

Figur e 6. Relationship between seed dormancy and summer precipitation. Data are population means.

(F1,33 = 16.16 and P = 0.0003; R2 = 0.31). There were some outlier populations that were quite dormant but received a consid- erable amount of precipitation (Mog and Sk-1), or were nondor- mant but received considerably more precipitation than the other populations (Veg-1 and Veg-2). These outliers did not drive the relationship, as excluding them increased the R2 to 0.41. Setting the small negative values for some nondormant populations to zero had almost no effect. Because the climate of the Central Asian populations is quite different from Western Europe, it makes sense to compare only the European populations, which form a cline, therefore only the European populations were used. However, the relationship remained significant when the Cen- tral Asian populations were included (P = 0.0011, R2 = 0.22). The effect of summer precipitation was the strongest for D25, but remains significant for D50 (P = 0.005, R2 = 0.16). Fur- thermore, summer precipitation had an effect even when it was included in a model with geographic region as a factor (P = 0.044, R2 = 0.29). We also investigated if including population struc- ture, as means of principle component analysis (PCA) compo- nents that were used in the association study for each popula- tion, had any effect on the model. We included the first two components. In a model with D25, summer precipitation and the two PCA components, only summer precipitation had sig- nificant effect on dormancy (P = 0.007, R2 = 0.27). The two PCA components were not significant (P = 0.968 and P = 0.741 for components 1 and 2, respectively). When both latitude and summer precipitation were included in the model, only summer precipitation had a significant effect (P = 0.034, R2 of the full model was 0.30) and the effect of latitude was not significant in this model (P = 0.502). In such a dataset many environmen- tal variables are correlated with latitude. However, the effect of summer precipitation seems to be the main factor because it is the only one that remains significant when latitude or temper- ature are analyzed jointly with summer precipitation (see Sup- porting information for details). Additionally, the correlation of seed dormancy to summer precipitation is stronger than to any of the nine other climatic parameters we tested (Table S6). Five other phenotypic traits were scored in the same common garden

I LKKA KR ONHOLM E T A L .

Ta b l e 7 . Correlations between genetic differentiation and ge- ography. Pairwise FST between populations, for SNPs or DOG1, correlated either to absolute differences in summer precipitation. Significance of correlations was tested with the Mantel test, 1000 permutations.

Precipitation Precipitation

Region versus SNP FST versus DOG1 8ST

European regions r=−0.0626 r=0.1229 p=0.666 p=0.055

Spain and France r=−0.0785 r=0.1385 p=0.704 p=0.053

France and Norway r=0.1299 r=0.1715 p=0.256 p=0.002

experiment (flowering time, number of basal and lateral branches, plant height at maturity, seed weight) and seed dormancy was the only phenotypic trait correlated with summer precipitation (Table S7).

We also tested for selection on DOG1 by comparing ge- netic distances to environmental differences. If genetic diver- gence between populations increases as an environmental vari- able changes, a stronger divergence for functional variation than for neutral variation might suggest that selection is operating. Neutral divergence did not correlate with summer precipitation differences between populations, but DOG1 divergence increased slightly with increased differences in summer precipitation. This relationship is only suggestive for all European regions together or when the Spanish and the French populations were used. How- ever, when the Norwegian and the French populations are com- pared the correlation is weak but significant (Table 7). This result further suggests that DOG1 variation in these populations is not neutral.

Discussion We report here a series of population genetics and functional genetic analyses that collectively bring a strong indication that DOG1 is subject to local selection in A. thaliana, thereby empha- sizing the importance of studying local adaptation with an array of approaches. Below, we first show that our results are robust to the major caveats associated with FST -based approaches and then discuss the possible reasons for the discordant result of the QST /FST analysis. We further review all other lines of evidence that support local adaptation for DOG1. Finally, we discuss our results in the light of population genetics models and highlight their implications for our understanding of local adaptation in general.

Our study provides several lines of evidence that consistently support that natural selection has shaped variation at DOG1. First, 8ST for DOG1 was higher than expected from neutral markers

(Fig. 3). Estimates of neutral FST could be in some instances underestimated as a consequence of ascertainment bias in the choice of SNP markers (Clark et al. 2005). However, we believe it is unlikely that SNP ascertainment has a large effect on FST

estimates in our study. The selected SNPs indeed tended to have a high frequency throughout the species range, but we observed in a previous study that FST estimates were not biased (Kronholm et al. 2010). This is presumably because SNP markers used here were selected from a sample that included genotypes from many differ- ent locations. Moreover, the microsatellite markers do not suffer from such a bias, because microsatellites have a high mutation rate. If a microsatellite locus is polymorphic in a panel of geno- types it is likely to be polymorphic in a another set of genotypes. The mean 8ST of the microsatellite markers is nearly equal to the mean FST of the SNP markers. For the European populations, microsatellite 8ST = 0.660 and SNP FST = 0.621, this again, sug- gests that the SNP markers are unlikely to be greatly biased by ascertainment. Importantly, we also investigated whether DOG1 is under local selection by examining 8ST along the chromosome at the position of DOG1. SNP markers along chromosome 5 were discovered from a panel of accessions from all regions used in this study. There was a clear peak in both 8ST and between-population heterozygosity (HT - HS ) at the position of DOG1 (Fig. 4). This provides a strong indication that the high 8ST of DOG1 is likely to have been caused by selection and not by a lower recombination rate in this part of the genome (Charlesworth et al. 1997).

If DOG1 is under spatially heterogeneous selection, variation at DOG1 should cause phenotypic variation; else natural selection could not act. DOG1 is a known QTL, but alleles present in previ- ous QTL mapping populations (Bentsink et al. 2010) are not nec- essarily representative of natural variation segregating throughout the species range. We therefore conducted an analysis of genetic association between DOG1 and dormancy. Several DOG1 alleles were associated with dormancy (Table 4) and these associations were confirmed by analyses of cosegregation between DOG1 and dormancy in F2 populations (Table 5). These results are therefore in agreement with the idea that the high FST observed at DOG1 was caused by natural selection on dormancy. Importantly, the F2

populations show at least four functional classes of alleles segre- gate in the population. Placing the functional differences on the haplotype network of DOG1 suggest that mutations modifying dormancy have originated several times independently from the haplotype 5, as suggested for haplotypes 1, 15 and the branch leading to haplotypes 18 and 19. In addition, haplotype 4 appears to increase dormancy and haplotype 21, decreases dormancy rela- tive to haplotype 4 from which it is derived. Haplotypes 2 and 22, which may result from a recombination event, were associated with opposite effects on the phenotype, suggesting that recombi- nation can also participate to the generation of novel functional

alleles. However, because all other mutations are in complete linkage disequilibrium, the series of alleles found to associate with dormancy is unlikely to be explained by recombination alone. Given that DOG1 is a small gene and recombination was found also to be rare along the full DOG1 sequence (M. De- bieu, unpubl. ms.), it appears that functionally different alleles in DOG1 have evolved independently, to either increase or decrease dormancy.

Classical comparative analysis of QST /FST estimates of pop- ulation differentiation could not reject the hypothesis that seed dormancy variation departs from neutral evolution, although this approach has proved successful in a number of other studies (Merila and Crnokrak 2001; Leinonen et al. 2008). Because QST

has both high sampling variance and high evolutionary variance (O’Hara and Merila 2005; Goudet and Bu chi 2006; Miller et al. 2008; Whitlock 2008), our result may simply reflect the limited power of this approach (Whitlock 1999; Goudet and Bu chi 2006; Goudet and Martin 2007; Miller et al. 2008). Both experimen- tal and theoretical studies have shown that finding evidence for local adaptation is very difficult when neutral FST is very high (Le Corre and Kremer 2003; Porcher et al. 2006). Arabidopsis is highly structured (Nordborg et al. 2005; Pico et al. 2008; Platt et al. 2010) and this is the case for our populations as well (Kronholm et al. 2010). Other studies in A. thaliana have also failed to find QST > FST (Kuittinen et al. 1997; Stenoien et al. 2005, but see Banta et al. 2007).

Demographic events can increase the variance of summary statistics such as FST across the genome, so the possibility that the pattern we observed in DOG1 is due to chance alone can- not be completely discarded. However, the adaptive relevance of DOG1 is also supported by independent findings. In a field study conducted at two locations in North America, QTLs for germination timing and fitness colocalized with DOG1 (Donohue et al. 2005b; Huang et al. 2010). The genotypes used were not local to the field sites, preventing inference of local adaptation, but show that variation in DOG1 can associate with substantial fitness effects. Here, the analysis of covariation between seed dor- mancy and the environment brings a novel indication that seed dormancy and DOG1 are subject to local selective forces. We observed a negative correlation between seed dormancy and the amount of precipitation received in the summer months (Fig. 6). Variation in DOG1 showed a similar trend in Norway and France (Table 7). Importantly, neutral markers were not correlated with summer precipitation, supporting the hypothesis that differences in dormancy between populations do not reflect only the action of genetic drift (Table 7). This finding also reveals the putative ecological forces acting on DOG1 evolution. It fits ecological pre- dictions for dormancy: plants can avoid summer drought by not germinating in the spring (Baskin and Baskin 1972; Evans and Ratcliffe 1972; Baskin and Baskin 1983).

The relationship between summer precipitation and dor- mancy was stronger for D25 than for D50, a result suggesting that summer precipitation is important in determining the time when seeds can begin germination. In A. thaliana, the environ- ment is known to influence seed dormancy induction, and can act to prevent early spring or summer germination and favor ger- mination in the fall (Montesinos et al. 2009). In Digitaria mi- lanjiana, the amount of total precipitation was related to seed dormancy, although a limited number of populations were stud- ied (Hacker 1984; Hacker et al. 1984). In contrast, germination of chilled seeds of Artemisia tridentata correlated with mean January temperature (Meyer and Monsen 1991). Furthermore, a relationship between germination patterns and the environment was found for Linum perenne (Meyer and Kitchen 1994) and in several species of Penstemon (Meyer et al. 1995). However, corre- lations between dormancy and environmental factors have not al- ways been found (Schu tz and Milberg 1997; Petru◦ and Tielbo rger 2008).

Our results may also have bearings on our understanding of the process of local adaptation in general. By using simulations, Yeaman and Guillaume (2009) showed that a genetic model with multiple alleles per locus, where allelic effects can freely evolve, permitted local adaptation in the presence of stronger gene flow than a model with biallelic loci or with a Gaussian approxima- tion of the phenotype. As QTL effects can be larger this also permits larger selection coefficients for individual loci. Conse- quently, larger differences can be maintained in the presence of gene flow (Yeaman and Guillaume 2009). By showing that DOG1 evolution fits better to a model with multiple alleles per locus, our results also find a broader significance, beyond the mere analysis of dormancy evolution. The situation we observe for DOG1 may be relatively common. A similar pattern has been found in the multiple independent loss-of-function mutations segregating for the gene FRIGIDA (Johanson et al. 2000; Le Corre et al. 2002; Le Corre 2005; Toomajian et al. 2006). When migration between populations is low relative to mutation rate, that is 2Ne μ > 2Ne m, adaptation is predicted to result from the fixation of indepen- dent beneficial mutations in different parts of the species range (Pennings and Hermisson 2006). This happens because gene flow is too small relative to mutation rate to allow for the same allele to spread to all populations where it would be beneficial. Therefore, many models in quantitative genetics, for example, Spichtig and Kawecki (2004), do not recapitulate adequately the whole pro- cess of local adaptation. Loss-of-function alleles, as in the case of FRIGIDA, are likely to arise readily by mutations. At DOG1 we have not observed any loss-of-function alleles. Yet, muta- tions seem to frequently generate functional variation at this gene. Studies on natural variation in A. thaliana do indeed hold great promise for elucidating the genetic basis of adaptation (Koornneef et al. 2004). Further studies of developmental pathways

controlling adaptive traits will help explain why some genes are involved in adaptive evolution and not others.

ACKNOWLEDGMENTS We would like to thank U. Tartler and A. O. Fandin o for technical assis- tance. S. Antoniazza kindly provided an R-script to calculate geographic distances between sets of coordinates. B. O’Hara gave advice on Bayesian models and QST . IK and JdM were funded by the Max Planck Society and SFB-680. CAB and FXP were funded by Ministry of Science and Innovation of Spain, project references: BIO2010-15022 and CGL2009- 07847/BOS, respectively. We thank three anonymous referees who gave constructive comments that improved the quality of the manuscript.

LITERATURE CITED Alonso-Blanco, C., L. Bentsink, C. J. Hanhart, H. Blankestijn-de Vries, and

M. Koornneef. 2003. Analysis of natural allelic variation at seed dor- mancy loci of Arabidopsis thaliana. Genetics 164:711–729.

Aydin, A., M. R. Toliat, S. Bahring, C. Becker, and P. Nurnberg. 2006. New universal primers facilitate pyrosequencing. Electrophoresis 27:394– 397.

Banta, J. A., J. Dole, M. B. Cruzan, and M. Pigliucci. 2007. Evidence of lo- cal adaptation to coarse-grained environmental variation in Arabidopsis thaliana. Evolution 61:2419–2432.

Baskin, J. M., and C. C. Baskin. 1972. Ecological life cycle and physiolog- ical ecology of seed germination of Arabidopsis thaliana. Can. J. Bot. 50:353–360.

———. 1983. Seasonal changes in the germination responses of buried seeds of Arabidopsis thaliana and ecological interpretation. Bot. Gaz. 144:540–543.

Beaumont, M. A. 2005. Adaptation and speciation: what can FST tell us. Trends Ecol. Evol. 20:435–440.

Bentsink, L., J. Jowett, C. J. Hanhart, and M. Koornneef. 2006. Cloning of DOG1, a quantitative trait locus controlling seed dormancy in Arabidop- sis. Proc. Natl. Acad. Sci. USA 103:17042–17047.

Bentsink, L., J. Hanson, C. J. Hanhart, H. Blankestijn-de Vries, C. Coltrane, P. Keizer, M. El-Lithy, C. Alonso-Blanco, M. T. de Andres, M. Reymond, et al. 2010. Natural variation for seed dormancy in Ara- bidopsis is regulated by additive genetic and molecular pathways. Proc. Natl. Acad. Sci. USA 107:4264–4269.

Biere, A. 1991. Parental effects in Lychnis flos-cuculi. II: selection on time of emergence and seedling performance in the field. J. Evol. Biol. 4:467– 486.

Bonnin, I., J. M. Prosperi, and I. Olivieri. 1996. Genetic markers and quanti- tative genetic variation in Medicago truncatula (Leguminosae): a com- parative analysis of population structure. Genetics 143:1795–805.

Bradbury, P. J., Z. Zhang, D. E. Kroon, T. M. Casstevens, Y. Ramdoss, and E. S. Buckler. 2007. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23:2633–2635.

Cadman, C. S., P. E. Toorop, H. W. Hilhorst, and W. E. Finch-Savage. 2006. Gene expression profiles of Arabidopsis Cvi seeds during dormancy cycling indicate a common underlying dormancy control mechanism. Plant J. 46:805–822.

Charlesworth, B., M. Nordborg, and D. Charlesworth. 1997. The effects of lo- cal selection, balanced polymorphism and background selection on equi- librium patterns of genetic diversity in subdivided populations. Genet. Res. 70:155–174.

Clark, A. G., M. J. Hubisz, C. Bustamante, S. Williamson, and R. Nielsen. 2005. Ascertainment bias in studies of human genome-wide polymor- phism. Genome Res. 15:1496–1502.

Clausen, J., D. D. Keck, and W. M. Hiesey. 1941. Regional differentiation in plant species. Am. Nat. 75:231–250.

Clement, M., D. Posada, and K. A. Crandall. 2000. TCS: a computer program to estimate gene genealogies. Mol. Ecol. 9:1657–1659.

Crawley, M. J. 2005. Statistics: an introduction using R. Wiley, New York. Donohue, K. 2002. Germination timing influences natural selection on life-

history characters in Arabidopsis thaliana. Ecology 83:1006–1016. Donohue, K., L. Dorn, C. Griffith, E. Kim, A. Aguilera, C. R. Polisetty,

and J. Schmitt. 2005a. Environmental and genetic influences on the germination of Arabidopsis thaliana in the field. Evolution 59:740–757.

———. 2005b. The evolutionary ecology of seed germination of Arabidopsis thaliana: variable natural selection on germination timing. Evolution 59:758–770.

Evans, A. S., and R. J. Cabin. 1995. Can dormancy affect the evolution of post-germination traits? The case of Lesquerella fendleri. Ecology 76:344–356.

Evans, J., and D. Ratcliffe. 1972. Variation in ’after-ripening’ and its ecological significance. Arabidopsis Information Service 9:3–5.

Excoffier, L. 2007. Analysis of population subdivision. In D. J. Balding, M. Bishop, and C. Cannings, eds. Handbook of statistical genetics. Wiley, Hoboken, NJ.

Fakhrai-Rad, H., N. Pourmand, and M. Ronaghi. 2002. PyrosequencingTM : an accurate detection platform for single nucleotide polymorphisms. Hum. Mutat. 19:479–485.

Finch-Savage, W. E., and G. Leubner-Metzger. 2006. Seed dormancy and the control of germination. New Phytol. 171:501–523.

Goudet, J. 2001. FSTAT, a program to estimate and test gene diversities and fixation indices (version 2.9.3). Available at http://www2.unil.ch/popgen/softwares/fstat.htm. Accessed February 17, 2012.

Goudet, J., and L. Bu chi. 2006. The effects of dominance, regular inbreeding and sampling design on QST , an estimator of population differentiation for quantitative traits. Genetics 172:1337–1347.

Goudet, J., and G. Martin. 2007. Under neutrality, QST ≤ FST when there is dominance in an island model. Genetics 176:1371–1374.

Griffith, C., E. Kim, and K. Donohue. 2004. Life-history variation and adapta- tion in the historically mobile plant Arabidopsis thaliana (Brassicaceae) in North America. Am. J. Bot. 91:837–849.

Griswold, C. K. 2006. Gene flow’s effect on the genetic architecture of a local adaptation and its consequences for QTL analyses. Heredity 96:445– 453.

Gross, K. L., and A. D. Smith. 1991. Seed mass and emergence time effects on performance of Panicum dichotomiflorum Michx across environments. Oecologia 87:270–278.

Hacker, J. B. 1984. Genetic variation in seed dormancy in Digitaria milanjiana in relation to rainfall at the collection site. J. Appl. Ecol. 21:947–959.

Hacker, J. B., M. H. Andrew, J. G. McIvor, and J. J. Mott. 1984. Evaluation in contrasting climates of dormancy characteristics of seed of Digitaria milanjiana. J. Appl. Ecol. 21:961–969.

Hall, T. A. 1999. BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucl. Acids Symp. Ser. 41:95–98.

Hijmans, R. J., L. Guarino, M. Cruz, and E. Rojas. 2001. Computer tools for spatial analysis of plant genetic resources data: 1. DIVA-GIS. Plant Genet. Res. Newsl. 127:15–19.

Hijmans, R. J., S. E. Cameron, J. L. Parra, P. G. Jones, and A. Jarvis. 2005. Very high resolution interpolated climate surfaces for global land areas. Int. J. Clim. 25:1965–1978.

Holm, S. 1979. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6:65–70.

Holsinger, K. E., and B. S. Weir. 2009. Genetics in geographically structured populations: defining, estimating and interpreting FST . Nat. Rev. Genet. 10:639–650.

Huang, X., J. Schmitt, L. Dorn, C. Griffith, S. Effgen, S. Takao, M. Koornneef, and K. Donohue. 2010. The earliest stages of adaptation in an experi- mental plant population: strong selection on QTLs for seed dormancy. Mol. Ecol. 19:1335–1351.

Johanson, U., J. West, C. Lister, S. Michaels, R. Amasino, and C. Dean. 2000. Molecular analysis of FRIGIDA, a major determi- nant of natural variation in Arabidopsis flowering time. Science 290: 344–347.

Kalisz, S. 1986. Variable selection on the timing of germination in Collinsia verna (Scrophulariaceae). Evolution 40:479–491.

Kawecki, T. J., and D. Ebert. 2004. Conceptual issues in local adaptation. Ecol. Lett. 7:1225–1241.

Koornneef, M., C. Alonso-Blanco, and D. Vreugdenhil. 2004. Naturally occurring genetic variation in Arabidopsis thaliana. Annu. Rev. Plant Biol. 55:141–172.

Kronholm, I., O. Loudet, and J. de Meaux. 2010. Influence of mutation rate on estimators of genetic differentiation—lessons from Arabidop- sis thaliana. BMC Genet. 11:33.

Kuittinen, H., A. Mattila, and O. Savolainen. 1997. Genetic variation at marker loci and in quantitative traits in natural populations of Arabidopsis thaliana. Heredity 79(Pt. 2):144–152.

Lande, R. 1992. Neutral theory of quantitative genetic variance in an island model with local extinction and colonization. Evolution 46:381–389.

Le Corre, V. 2005. Variation at two flowering time genes within and among populations of Arabidopsis thaliana: comparison with markers and traits. Mol. Ecol. 14:4181–4192.

Le Corre, V., and A. Kremer. 2003. Genetic variability at neutral markers, quantitative trait land trait in a subdivided population under selection. Genetics 164:1205–1219.

Le Corre, V., F. Roux, and X. Reboud. 2002. DNA polymorphism at the FRIGIDA gene in Arabidopsis thaliana: extensive nonsynonymous vari- ation is consistent with local selection for flowering time. Mol. Biol. Evol. 19:1261–1271.

Leimu, R., and M. Fischer. 2008. A meta-analysis of local adaptation in plants. PLoS One 3:e4010.

Leinonen, T., R. B. O’Hara, J. M. Cano, and J. Merila. 2008. Comparative stud- ies of quantitative trait and neutral marker divergence: a meta-analysis. J. Evol. Biol. 21:1–17.

Lunn, D. J., A. Thomas, N. Best, and D. Spiegelhalter. 2000. WinBUGS—a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and Computing 10:325–337.

Lynch, M., and B. Walsh. 1998. Genetics and the analysis of quantitative traits. Sinauer Associates, Inc., Sunderland.

Marks, M., and S. Prince. 1981. Influence of germination date on survival and fecundity in wild lettuce Lactuca serriola. Oikos 36:326–330.

Merila, J., and P. Crnokrak. 2001. Comparison of genetic differentiation at marker loci and quantitative traits. J. Evol. Biol. 14:892–903.

Meyer, S. E., and S. G. Kitchen. 1994. life history variation in blue flax (Linum perenne: Linaceae): seed germination phenology. Am. J. Bot. 81:528–535.

Meyer, S. E., and S. B. Monsen. 1991. Habitat-correlated variation in mountain big sagebrush (Artemisia tridentata ssp. vaseyana) seed germination patterns. Ecology 72:739–742.

Meyer, S. E., S. G. Kitchen, and S. L. Carlson. 1995. Seed germination timing patterns in intermountain Penstemon (Scrophulariaceae). Am. J. Bot. 82:377–389.

Michalakis, Y., and L. Excoffier. 1996. A generic estimation of population subdivision using distances between alleles with special reference for microsatellite loci. Genetics 142:1061–1064.

Miller, J. R., B. P. Wood, and M. B. Hamilton. 2008. FST and QST under neutrality. Genetics 180:1023–1037.

Mitchell-Olds, T., and J. Schmitt. 2006. Genetic mechanisms and evolu- tionary significance of natural variation in Arabidopsis. Nature 441: 947–952.

Montesinos, A., S. J. Tonsor, C. Alonso-Blanco, and F. X. Pico. 2009. Demographic and genetic patterns of variation among populations of Arabidopsis thaliana from contrasting native environments. PLoS One 4:e7213.

Munir, J., L. A. Dorn, K. Donohue, and J. Schmitt. 2001. The effect of maternal photoperiod on seasonal dormancy in Arabidopsis thaliana (Brassicaceae). Am. J. Bot. 88:1240–1249.

Nordborg, M., T. T. Hu, Y. Ishino, J. Jhaveri, C. Toomajian, H. Zheng, E. Bakker, P. Calabrese, J. Gladstone, R. Goyal, et al. 2005. The pattern of polymorphism in Arabidopsis thaliana. PLoS Biol. 3:e196.

O’Hara, R. B., and J. Merila. 2005. Bias and precision in QST estimates: problems and some solutions. Genetics 171:1331–1339.

Oksanen, J., R. Kindt, P. Legendre, R. B. O’Hara, M. Henry, and M. H. H. Stevens. 2007. Vegan: community ecology package. R package version 1.8-7. Available at http://cran.r-project.org/web/packages. Ac- cessed February 17, 2012.

Orr, H. A. 1998. The population genetics of adaptation: the distribution of factors fixed during adaptive evolution. Evolution 52:935–949.

———. 2005. The genetic theory of adaptation: a brief history. Nat. Rev. Genet. 6:119–127.

Pennings, P. S., and J. Hermisson. 2006. Soft sweeps II—molecular population genetics of adaptation from recurrent mutation or migration. Mol. Biol. Evol. 23:1076–1084.

Petru◦, M., and K. Tielbo rger. 2008. Germination behaviour of annual plants under changing climatic conditions: separating local and regional envi- ronmental effects. Oecologia 155:717–728.

Pico, F. X., B. Mendez-Vigo, J. M. Martinez-Zapater, and C. Alonso-Blanco. 2008. Natural genetic variation of Arabidopsis thaliana is geographically structured in the Iberian Peninsula. Genetics 180:1009–1021.

Platt, A., M. Horton, Y. S. Huang, Y. Li, A. E. Anastasio, N. W. Muly- ati, J. Agren, O. Bossdorf, D. Byers, K. Donohue, et al. 2010. The scale of population structure in Arabidopsis thaliana. PLoS Genet. 6: e1000843.

Porcher, E., T. Giraud, and C. Lavigne. 2006. Genetic differentiation of neu- tral markers and quantitative traits in predominantly selfing metapopu- lations: confronting theory and experiments with Arabidopsis thaliana. Genet. Res. 87:1–12.

R Development Core Team. 2006. R: A language and environment for statisti- cal computing. R Foundation for Statistical Computing, Vienna, Austria. Available at http://www.R-project.org.

Rozas, J., J. C. Sanchez-DelBarrio, X. Messeguer, and R. Rozas. 2003. DnaSP, DNA polymorphism analyses by the coalescent and other methods. Bioinformatics 19:2496–2497.

Schu tz, W., and P. Milberg. 1997. Seed dormancy in Carex canescens: Re- gional differences and ecological consequences. Oikos 78:420–428.

Slatkin, M. 1995. A measure of population subdivision based on microsatellite allele frequencies. Genetics 139:457–462.

Spichtig, M., and T. Kawecki. 2004. The maintenance (or not) of polygenic variation by soft selection in heterogeneous environments. Am. Nat. 164:70–84.

Spitze, K. 1993. Population structure in Daphnia obtusa: Quantitative genetic and allozymic variation. Genetics 135:367–374.

Stenoien, H. K., C. B. Fenster, A. Tonteri, and O. Savolainen. 2005. Genetic variability in natural populations of Arabidopsis thaliana in northern Europe. Mol. Ecol. 14:137–148.

Stich, B., J. Mohring, H.-P. Piepho, M. Heckenberger, E. S. Buckler, and A. E. Melchinger. 2008. Comparison of mixed-model approaches for association mapping. Genetics 178:1745–1754.

Toomajian, C., T. T. Hu, M. J. Aranzana, C. Lister, C. Tang, H. Zheng, K. Zhao, P. Calabrese, C. Dean, and M. Nordborg. 2006. A nonparametric test reveals selection for rapid flowering in the Arabidopsis genome. PLoS Biol. 4:e137.

Turesson, G. 1922. The genotypical response of the plant species to the habitat. Hereditas 3:211–350.

———. 1925. The plant species in relation to habitat and climate. Hereditas 6:147–236.

Venables, W. N., and B. D. Ripley. 2002. Modern applied statistics with S. Springer, New York.

Weir, B. S., and C. C. Cockerham. 1984. Estimating F-statistics for the analysis of populations structure. Evolution 38:1358–1370

Whitlock, M. C. 1999. Neutral additive genetic variance in a metapopulation. Genet. Res. 74:215–221.

———. 2008. Evolutionary inference from QST . Mol. Ecol. 17:1885–1896. ———. 2011. G’ST and D do not replace FST . Mol. Ecol. 20:1083–1091. Wilczek, A. M., J. L. Roe, M. C. Knapp, M. D. Cooper, C. Lopez-Gallego,

L. J. Martin, C. D. Muir, S. Sim, A. Walker, J. Anderson, et al. 2009. Effects of genetic perturbation on seasonal life history plasticity. Science 323:930–934.

Yeaman, S., and F. Guillaume. 2009. Predicting adaptation under migration load: the role of genetic skew. Evolution 63:2926–2938.

Yu, J., G. Pressoir, W. H. Briggs, I. Vroh Bi, M. Yamasaki, J. F. Doebley, M. D. McMullen, B. S. Gaut, D. M. Nielsen, J. B. Holland, et al. 2006. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat. Genet. 38:203–208.

Associate Editor: J. Kelly

Supporting Information The following supporting information is available for this article:

Figure S1. Distribution of harvesting dates for the common garden experiment. Figure S2. An example of population structure correction. Figure S3. Amino acid alignment of exon 1 of DOG1. Table S1. Primers used to amplify and sequence the fragments for SNP discovery. Table S2. Primer sequences used in the pyrosequencing assays. Table S3. Minimum MSD values and their corresponding T values for different traits and samples. Table S4. Polymorphic positions in the observed DOG1 haplotypes. Table S5. Population means and genetic variation within populations for seed dormancy. Table S6. Relationship between several environmental variables and seed dormancy (D25). Table S7. Relationship between other phenotypic traits and summer precipitation.

Supporting Information may be found in the online version of this article.

Please note: Wiley-Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article.